Prediction of seasonal snow accumulation in cold climate forests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Accumulation of snow under forest canopies is known to decline with increasing canopy density and leaf area because of snow interception and sublimation in the canopy. Seasonal snow accumulation measurements, collected over a decade from various forest stands in western Canada, were used to test and develop methods to relate forest snow accumulation to stand properties and observations of either small‐clearing seasonal snow accumulation or seasonal snowfall. At sub‐stand scales, the variability of seasonal snow accumulation was not well related to stand leaf area, seasonal interception or small‐clearing seasonal snow accumulation. At the stand scale, physically based snow interception equations predicted seasonal snow accumulation from the stand leaf area and the seasonal snow accumulation or snowfall in adjacent clearings. A simple parametric form of these equations showed the sensitivity of seasonal snow accumulation to leaf area at the forest stand scale and suggested a relationship to extrapolate snow accumulation or snowfall measurements from clearings to forests. These relationships, developed from Canadian boreal forest observations, are consistent with Kuz'min's (1960. Formirovanie Snezhnogo Pokrova i Metody Opredeleniya Snegozapasov . Gidrometeoizdat: Leningrad) relationship between accumulation and canopy density derived from Russian observations, suggesting a good degree of transferability. Copyright © 2002 Crown in the right of Canada. Published by John Wiley & Sons Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it